A history-conditioned latent motion prior enables real-world dexterous hand policies to improve from 56% to 99% success via residual reinforcement learning without breaking contact.
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LAMP: Latent Motion Prior-Guided Real-World Learning for Dexterous Hand Manipulation
A history-conditioned latent motion prior enables real-world dexterous hand policies to improve from 56% to 99% success via residual reinforcement learning without breaking contact.